import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


__a    :	List[Any]         =    1e-4

if is_torch_available():
		import torch

		from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
		from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder




@require_torch
class   _UpperCamelCase :


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=13 ,  lowerCAmelCase__=7 ,  lowerCAmelCase__=14 ,  lowerCAmelCase__=10 ,  lowerCAmelCase__=19 ,  lowerCAmelCase__=5 ,  lowerCAmelCase__=4 ,  lowerCAmelCase__=True ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=2 ,  lowerCAmelCase__=4 ,  lowerCAmelCase__=4 ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=[1, 2, 3, 4, 5] ,  lowerCAmelCase__=25 ,  lowerCAmelCase__=5 ,  )	->   int:

							'''simple docstring'''


							__lowercase					     =				d_model
							__lowercase					     =				parent
							__lowercase					     =				batch_size
							__lowercase					     =				prediction_length
							__lowercase					     =				context_length
							__lowercase					     =				cardinality
							__lowercase					     =				num_time_features
							__lowercase					     =				lags_sequence
							__lowercase					     =				embedding_dimension
							__lowercase					     =				is_training
							__lowercase					     =				hidden_size
							__lowercase					     =				num_hidden_layers
							__lowercase					     =				num_attention_heads
							__lowercase					     =				intermediate_size
							__lowercase					     =				hidden_act
							__lowercase					     =				hidden_dropout_prob
							__lowercase					     =				attention_probs_dropout_prob

							__lowercase					     =				context_length
							__lowercase					     =				prediction_length + label_length
							__lowercase					     =				label_length

							__lowercase					     =				moving_average
							__lowercase					     =				autocorrelation_factor





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							return AutoformerConfig(
							    d_model=self.d_model ,  encoder_layers=self.num_hidden_layers ,  decoder_layers=self.num_hidden_layers ,  encoder_attention_heads=self.num_attention_heads ,  decoder_attention_heads=self.num_attention_heads ,  encoder_ffn_dim=self.intermediate_size ,  decoder_ffn_dim=self.intermediate_size ,  dropout=self.hidden_dropout_prob ,  attention_dropout=self.attention_probs_dropout_prob ,  prediction_length=self.prediction_length ,  context_length=self.context_length ,  label_length=self.label_length ,  lags_sequence=self.lags_sequence ,  num_time_features=self.num_time_features ,  num_static_categorical_features=1 ,  cardinality=[self.cardinality] ,  embedding_dimension=[self.embedding_dimension] ,  moving_average=self.moving_average ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				config.context_length + max(config.lags_sequence		)

							__lowercase					     =				ids_tensor([self.batch_size, 1] ,  config.cardinality[0]		)
							__lowercase					     =				floats_tensor([self.batch_size, _past_length, config.num_time_features]		)
							__lowercase					     =				floats_tensor([self.batch_size, _past_length]		)
							__lowercase					     =				floats_tensor([self.batch_size, _past_length]		) > 0.5

							# decoder inputs
							__lowercase					     =				floats_tensor([self.batch_size, config.prediction_length, config.num_time_features]		)
							__lowercase					     =				floats_tensor([self.batch_size, config.prediction_length]		)

							__lowercase					     =				{
							    '''past_values''': past_values,
							    '''static_categorical_features''': static_categorical_features,
							    '''past_time_features''': past_time_features,
							    '''past_observed_mask''': past_observed_mask,
							    '''future_time_features''': future_time_features,
							    '''future_values''': future_values,
							}
							return inputs_dict





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				self.get_config()
							__lowercase					     =				self.prepare_autoformer_inputs_dict(lowerCAmelCase__		)
							return config, inputs_dict





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							__lowercase     ,	__lowercase					     =				self.prepare_config_and_inputs()
							return config, inputs_dict





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Tuple:

							'''simple docstring'''


							__lowercase					     =				AutoformerModel(config=lowerCAmelCase__		).to(lowerCAmelCase__		).eval()
							__lowercase					     =				model(**lowerCAmelCase__		)

							__lowercase					     =				outputs.encoder_last_hidden_state
							__lowercase					     =				outputs.last_hidden_state

							with tempfile.TemporaryDirectory() as tmpdirname:
											__lowercase					     =				model.get_encoder()
											encoder.save_pretrained(lowerCAmelCase__		)
											__lowercase					     =				AutoformerEncoder.from_pretrained(lowerCAmelCase__		).to(lowerCAmelCase__		)

							__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase					     =				model.create_network_inputs(**lowerCAmelCase__		)
							__lowercase     ,	__lowercase					     =				model.decomposition_layer(transformer_inputs[:, : config.context_length, ...]		)

							__lowercase					     =				torch.cat(
							    (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...]) ,  dim=-1 ,  )
							__lowercase					     =				encoder(inputs_embeds=lowerCAmelCase__		)[0]
							self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3		)

							__lowercase					     =				(
							    torch.mean(transformer_inputs[:, : config.context_length, ...] ,  dim=1		)
							    .unsqueeze(1		)
							    .repeat(1 ,  config.prediction_length ,  1		)
							)
							__lowercase					     =				torch.zeros(
							    [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]] ,  device=enc_input.device ,  )

							__lowercase					     =				torch.cat(
							    (
							        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros) ,  dim=1		),
							        feature[:, config.context_length - config.label_length :, ...],
							    ) ,  dim=-1 ,  )
							__lowercase					     =				torch.cat(
							    (
							        torch.cat((trend_input[:, -config.label_length :, ...], mean) ,  dim=1		),
							        feature[:, config.context_length - config.label_length :, ...],
							    ) ,  dim=-1 ,  )

							with tempfile.TemporaryDirectory() as tmpdirname:
											__lowercase					     =				model.get_decoder()
											decoder.save_pretrained(lowerCAmelCase__		)
											__lowercase					     =				AutoformerDecoder.from_pretrained(lowerCAmelCase__		).to(lowerCAmelCase__		)

							__lowercase					     =				decoder(
							    trend=lowerCAmelCase__ ,  inputs_embeds=lowerCAmelCase__ ,  encoder_hidden_states=lowerCAmelCase__ ,  )[0]

							self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3		)




@require_torch
class   _UpperCamelCase (		_UpperCAmelCase		,_UpperCAmelCase		,unittest.TestCase					):


			"""simple docstring"""






			__a    :					Tuple     					=		(AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
			__a    :					List[str]     					=		(AutoformerForPrediction,) if is_torch_available() else ()
			__a    :					Optional[Any]     					=		{'''feature-extraction''': AutoformerModel} if is_torch_available() else {}
			__a    :					Optional[int]     					=		False
			__a    :					List[Any]     					=		False
			__a    :					Dict     					=		False
			__a    :					Optional[int]     					=		False
			__a    :					Optional[Any]     					=		False
			__a    :					str     					=		False





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							__lowercase					     =				AutoformerModelTester(self		)
							__lowercase					     =				ConfigTester(self ,  config_class=lowerCAmelCase__ ,  has_text_modality=lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Tuple:

							'''simple docstring'''


							self.config_tester.run_common_tests()





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase     ,	__lowercase					     =				self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
											__lowercase					     =				model_class(lowerCAmelCase__		)

											with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_pretrained(lowerCAmelCase__		)
															__lowercase     ,	__lowercase					     =				model_class.from_pretrained(lowerCAmelCase__ ,  output_loading_info=lowerCAmelCase__		)
											self.assertEqual(info['''missing_keys'''] ,  []		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[Any]:

							'''simple docstring'''


							__lowercase					     =				self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_encoder_decoder_model_standalone(*lowerCAmelCase__		)





			@unittest.skip(reason='''Model has no tokens embeddings'''		)
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[Any]:

							'''simple docstring'''


							pass





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[str]:

							'''simple docstring'''


							__lowercase					     =				inspect.signature(getattr(lowerCAmelCase__ ,  '''forward'''		)		)
							# The main input is the name of the argument after `self`
							__lowercase					     =				list(model_signature.parameters.keys()		)[1]
							self.assertEqual(AutoformerModel.main_input_name ,  lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   str:

							'''simple docstring'''


							__lowercase     ,	__lowercase					     =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowercase					     =				model_class(lowerCAmelCase__		)
											__lowercase					     =				inspect.signature(model.forward		)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											__lowercase					     =				[*signature.parameters.keys()]

											__lowercase					     =				[
											    '''past_values''',
											    '''past_time_features''',
											    '''past_observed_mask''',
											    '''static_categorical_features''',
											    '''static_real_features''',
											    '''future_values''',
											    '''future_time_features''',
											]

											if model.__class__.__name__ in ["AutoformerForPrediction"]:
															expected_arg_names.append('''future_observed_mask'''		)

											expected_arg_names.extend(
											    [
											        '''decoder_attention_mask''',
											        '''head_mask''',
											        '''decoder_head_mask''',
											        '''cross_attn_head_mask''',
											        '''encoder_outputs''',
											        '''past_key_values''',
											        '''output_hidden_states''',
											        '''output_attentions''',
											        '''use_cache''',
											        '''return_dict''',
											    ]		)

											self.assertListEqual(arg_names[: len(lowerCAmelCase__		)] ,  lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase     ,	__lowercase					     =				self.model_tester.prepare_config_and_inputs_for_common()
							__lowercase					     =				True

							__lowercase					     =				getattr(self.model_tester ,  '''seq_length''' ,  lowerCAmelCase__		)
							__lowercase					     =				getattr(self.model_tester ,  '''decoder_seq_length''' ,  lowerCAmelCase__		)
							__lowercase					     =				getattr(self.model_tester ,  '''encoder_seq_length''' ,  lowerCAmelCase__		)
							__lowercase					     =				getattr(self.model_tester ,  '''d_model''' ,  lowerCAmelCase__		)
							__lowercase					     =				getattr(self.model_tester ,  '''num_attention_heads''' ,  lowerCAmelCase__		)
							__lowercase					     =				d_model // num_attention_heads

							for model_class in self.all_model_classes:
											__lowercase					     =				True
											__lowercase					     =				False
											__lowercase					     =				True
											__lowercase					     =				model_class(lowerCAmelCase__		)
											model.to(lowerCAmelCase__		)
											model.eval()
											with torch.no_grad():
															__lowercase					     =				model(**self._prepare_for_class(lowerCAmelCase__ ,  lowerCAmelCase__		)		)
											__lowercase					     =				outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
											self.assertEqual(len(lowerCAmelCase__		) ,  self.model_tester.num_hidden_layers		)

											# check that output_attentions also work using config
											del inputs_dict["output_attentions"]
											__lowercase					     =				True
											__lowercase					     =				model_class(lowerCAmelCase__		)
											model.to(lowerCAmelCase__		)
											model.eval()
											with torch.no_grad():
															__lowercase					     =				model(**self._prepare_for_class(lowerCAmelCase__ ,  lowerCAmelCase__		)		)
											__lowercase					     =				outputs.encoder_attentions
											self.assertEqual(len(lowerCAmelCase__		) ,  self.model_tester.num_hidden_layers		)

											self.assertListEqual(
											    list(attentions[0].shape[-3:]		) ,  [self.model_tester.num_attention_heads, encoder_seq_length, dim] ,  )
											__lowercase					     =				len(lowerCAmelCase__		)

											__lowercase					     =				7

											if "last_hidden_state" in outputs:
															correct_outlen += 1

											if "trend" in outputs:
															correct_outlen += 1

											if "past_key_values" in outputs:
															correct_outlen += 1  # past_key_values have been returned

											if "loss" in outputs:
															correct_outlen += 1

											if "params" in outputs:
															correct_outlen += 1

											self.assertEqual(lowerCAmelCase__ ,  lowerCAmelCase__		)

											# decoder attentions
											__lowercase					     =				outputs.decoder_attentions
											self.assertIsInstance(lowerCAmelCase__ ,  (list, tuple)		)
											self.assertEqual(len(lowerCAmelCase__		) ,  self.model_tester.num_hidden_layers		)
											self.assertListEqual(
											    list(decoder_attentions[0].shape[-3:]		) ,  [self.model_tester.num_attention_heads, decoder_seq_length, dim] ,  )

											# cross attentions
											__lowercase					     =				outputs.cross_attentions
											self.assertIsInstance(lowerCAmelCase__ ,  (list, tuple)		)
											self.assertEqual(len(lowerCAmelCase__		) ,  self.model_tester.num_hidden_layers		)
											self.assertListEqual(
											    list(cross_attentions[0].shape[-3:]		) ,  [self.model_tester.num_attention_heads, decoder_seq_length, dim] ,  )

							# Check attention is always last and order is fine
							__lowercase					     =				True
							__lowercase					     =				True
							__lowercase					     =				model_class(lowerCAmelCase__		)
							model.to(lowerCAmelCase__		)
							model.eval()
							with torch.no_grad():
											__lowercase					     =				model(**self._prepare_for_class(lowerCAmelCase__ ,  lowerCAmelCase__		)		)

							self.assertEqual(out_len + 2 ,  len(lowerCAmelCase__		)		)

							__lowercase					     =				outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

							self.assertEqual(len(lowerCAmelCase__		) ,  self.model_tester.num_hidden_layers		)
							self.assertListEqual(
							    list(self_attentions[0].shape[-3:]		) ,  [self.model_tester.num_attention_heads, encoder_seq_length, dim] ,  )





			@is_flaky()
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[Any]:

							'''simple docstring'''


							super().test_retain_grad_hidden_states_attentions()





def 							UpperCAmelCase		(       lowercase="train-batch.pt"				):







				"""simple docstring"""



				__lowercase					     =				hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch'''				,     filename=lowercase				,     repo_type='''dataset'''				)
				__lowercase					     =				torch.load(lowercase				,     map_location=lowercase				)
				return batch




@require_torch
@slow
class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							__lowercase					     =				AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly'''		).to(lowerCAmelCase__		)
							__lowercase					     =				prepare_batch()

							with torch.no_grad():
											__lowercase					     =				model(
											    past_values=batch['''past_values'''] ,  past_time_features=batch['''past_time_features'''] ,  past_observed_mask=batch['''past_observed_mask'''] ,  static_categorical_features=batch['''static_categorical_features'''] ,  future_values=batch['''future_values'''] ,  future_time_features=batch['''future_time_features'''] ,  )[0]

							__lowercase					     =				torch.Size(
							    (64, model.config.prediction_length + model.config.label_length, model.config.feature_size)		)
							self.assertEqual(output.shape ,  lowerCAmelCase__		)

							__lowercase					     =				torch.tensor(
							    [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]] ,  device=lowerCAmelCase__		)
							self.assertTrue(torch.allclose(output[0, :3, :3] ,  lowerCAmelCase__ ,  atol=lowerCAmelCase__		)		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[int]:

							'''simple docstring'''


							__lowercase					     =				AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''		).to(lowerCAmelCase__		)
							__lowercase					     =				prepare_batch('''val-batch.pt'''		)
							with torch.no_grad():
											__lowercase					     =				model(
											    past_values=batch['''past_values'''] ,  past_time_features=batch['''past_time_features'''] ,  past_observed_mask=batch['''past_observed_mask'''] ,  static_categorical_features=batch['''static_categorical_features'''] ,  ).encoder_last_hidden_state
							__lowercase					     =				torch.Size((64, model.config.context_length, model.config.d_model)		)
							self.assertEqual(output.shape ,  lowerCAmelCase__		)

							__lowercase					     =				torch.tensor(
							    [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]] ,  device=lowerCAmelCase__		)
							self.assertTrue(torch.allclose(output[0, :3, :3] ,  lowerCAmelCase__ ,  atol=lowerCAmelCase__		)		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							__lowercase					     =				AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly'''		).to(lowerCAmelCase__		)
							__lowercase					     =				prepare_batch('''val-batch.pt'''		)
							with torch.no_grad():
											__lowercase					     =				model.generate(
											    static_categorical_features=batch['''static_categorical_features'''] ,  past_time_features=batch['''past_time_features'''] ,  past_values=batch['''past_values'''] ,  future_time_features=batch['''future_time_features'''] ,  past_observed_mask=batch['''past_observed_mask'''] ,  )
							__lowercase					     =				torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length)		)
							self.assertEqual(outputs.sequences.shape ,  lowerCAmelCase__		)

							__lowercase					     =				torch.tensor([3130.6763, 4056.5293, 7053.0786] ,  device=lowerCAmelCase__		)
							__lowercase					     =				outputs.sequences.mean(dim=1		)
							self.assertTrue(torch.allclose(mean_prediction[0, -3:] ,  lowerCAmelCase__ ,  rtol=1E-1		)		)
import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)

				__lowercase					     =				chkpt['''model''']

				# We have the base model one level deeper than the original XLM repository
				__lowercase					     =				{}
				for k, v in state_dict.items():
								if "pred_layer" in k:
												__lowercase					     =				v
								else:
												__lowercase					     =				v

				__lowercase					     =				chkpt['''params''']
				__lowercase					     =				{n: v for n, v in config.items() if not isinstance(lowercase				,     (torch.FloatTensor, numpy.ndarray)				)}

				__lowercase					     =				chkpt['''dico_word2id''']
				__lowercase					     =				{s + '''</w>''' if s.find('''@@'''				) == -1 and i > 13 else s.replace('''@@'''				,     ''''''				): i for s, i in vocab.items()}

				# Save pytorch-model
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + CONFIG_NAME
				__lowercase					     =				pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

				print(F"Save PyTorch model to {pytorch_weights_dump_path}"				)
				torch.save(lowercase				,     lowercase				)

				print(F"Save configuration file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)

				print(F"Save vocab file to {pytorch_config_dump_path}"				)
				with open(lowercase				,     '''w'''				,     encoding='''utf-8'''				) as f:
								f.write(json.dumps(lowercase				,     indent=2				) + '''\n'''				)


if __name__ == "__main__":
		__a    :	Optional[int]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--xlm_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		__a    :	Any         =    parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)